C1000-154 Online Dumps - IBM Watson Data Scientist v1 - Free Latest Dumps for IT Certification Exams

C1000-154 Online Dumps – IBM Watson Data Scientist v1

dumps2024-08-20T10:52:17+08:00

C1000-154 exam is a required test for earning the IBM Certified Data Scientist – Watson Specialist v1 certification. Studying IBM certification C1000-154 exam information is the first step for you to understand the test. There are 60 questions in the real IBM C1000-154 exam, and you have 90 minutes to answer all the questions. The number of questions required to pass the IBM C1000-154 exam is 41. The available language is English. The exam cost is $200. We provide the latest IBM Watson Data Scientist v1 C1000-154 exam online dumps, which are valuable for you to study. Free IBM Watson Data Scientist v1 C1000-154 exam online dumps are available below.

Page 1 of 3

1. When anticipating additional data sources that might be relevant, what is a crucial factor to consider?

The color scheme of the data visualization

The data source's popularity on social media

The relevance of the data source to the business problem

The graphical interface of the data source

2. A virtual assistant has been developed and deployed based on the Watson Assistant service. The assistant will support customers by answering FAQs (Frequent Answered Questions).

Which metric is a good indicator of the performance of the virtual assistant?

The Area Under the Curve (AUC)

Measure escalated calls using A/B testing

The Root Mean Squared Error (RMSE) of words

The F1 score of predicted intents in the Analytics tab

3. Which of the following is a critical first step in understanding a business problem for data science projects?

Selecting the machine learning algorithm

Defining the project scope

Choosing the visualization tools

Deploying the model

4. How can data splits be made reproducible in a machine learning experiment?

By using a different random seed each time the data is split

By partitioning the data manually

By using a consistent random seed when splitting the data

By splitting the data in a sequential manner without randomization

5. What is the key difference between batch processing and streaming in data processing?

Batch processing involves real-time data processing, whereas streaming does not process data

Streaming is suitable for large, historical datasets, whereas batch processing is for real-time data analysis

Batch processing processes data in large blocks at a time, whereas streaming processes data in real-time as it arrives

6. Which of the following is NOT a type of data source commonly integrated with Cloud Pak for Data?

Social media feeds

Proprietary in-memory databases

Paper-based records

Cloud storage services

7. When selecting a small number of algorithms based on model requirements, what factor should you primarily consider?

The popularity of the algorithm in recent academic papers.

Compatibility of the algorithm with the data characteristics and the predictive task.

The algorithm that requires the least amount of data preprocessing.

Choosing algorithms that are only based on supervised learning.

8. The first step in performing exploratory data analysis (EDA) typically involves:

Choosing a color palette for data visualization

Determining the hypothesis for the analysis

Connecting to as many data sources as possible

Selecting a random sample of data to analyze

9. In the context of deployment environments, understanding resources is crucial.

What does this typically involve?

Choosing the most aesthetically pleasing user interface

Determining the computational power and memory requirements for the deployed solution

Selecting the programming language with the least number of keywords

Focusing exclusively on the cost of storage

10. Which Python library is commonly used for data manipulation and analysis, and is available in Cloud Pak for Data?

TensorFlow

PyTorch

Pandas

Keras

Page 2 of 3

11. When helping businesses articulate and define problems, what is an essential first step?

Identifying potential data sources

Defining key performance indicators (KPIs)

Establishing a clear problem statement

Selecting the analytical techniques

12. An E-retailer uses several important data sources, including web logs which contain all of the information on how customers navigate the web site. There are non-informative entries in the web logs that need to be removed.

During which phase should these non-informative entries be removed in the CRISP-DM model?

Modeling

Data Preparation

Data Understanding

Business Understanding

13. What is a key disadvantage of using Grid Search for hyperparameter tuning?

It is too quick and may miss out on evaluating some hyperparameters

It requires no prior knowledge of the hyperparameters

It can be computationally expensive and time-consuming due to its exhaustive nature

It is unable to handle discrete parameters

14. Which method is used for merging records in SPSS Modeler Merge node that allows specifying a requirement to be satisfied in order for the merge to take place?

Key

Order

Filter

Condition

15. Why is it important to create data splits that are reproducible?

To ensure that each model run can be exactly replicated for verification and comparison

To guarantee that the model will perform with 100% accuracy on unseen data

To use more data for testing than for training

To allow for larger test sets for more comprehensive testing

16. In unsupervised learning, which algorithm is best suited for grouping customers based on their purchase history to target marketing efforts more effectively?

Support Vector Machines

K-Means Clustering

Linear Regression

Decision Trees

17. In the context of avoiding underfitting and overfitting, what role does splitting the data into training, testing, and validation sets play?

It ensures that the model is trained on the maximum amount of data possible

It allows for the model to be validated and tested on different subsets of data to check its generalization ability

It guarantees that the model will perform with 100% accuracy on unseen data

It increases the computational complexity without improving model performance

18. Which of the following is true about the AUC measure in the context of classification models?

It represents the degree of separability between classes.

It is less useful when the classes are highly imbalanced.

It indicates the number of false positives.

It measures the model's accuracy using a single threshold.

19. What is the primary purpose of partitioning data into training and test sets?

To ensure that the model gets exposed to all possible data scenarios during training

To maximize the accuracy of the model by using all data for training

To evaluate the model's performance on unseen data

To increase the computational efficiency of model training

20. Which analytic technique is NOT typically used to address business requirements?

Regression analysis

Clustering

Decision trees

Proofreading

Page 3 of 3

21. Which two packages can be used to customize the software configuration of a Jupyter notebook environment in Cloud Pak for Data?

vim

pip

sudo

bash

conda

22. Which statement describes bagging?

Building models and using their output as features into a final model.

Building models in parallel and aggregating their predictions to select the final prediction.

Building models sequentially and evaluating the success of earlier models. It combines a set of weak learners into a strong learner.

Building models with artificial neural networks based on the sharedweight architecture of the convolution kernels or filters.

23. Assessing the feasibility of a solution(s) often requires evaluating:

The color scheme of the user interface

Market competition only

Technical feasibility, cost, and time constraints

Preferred communication channels of the project manager

24. Which statement best differentiates machine learning from deep learning?

Machine learning algorithms perform better on structured data, while deep learning excels with unstructured data like images and text.

Deep learning algorithms require less data to learn.

Machine learning models are always transparent, whereas deep learning models cannot be interpreted.

Deep learning algorithms are a subset of machine learning algorithms that do not require feature engineering.

C1000-154 Online Dumps – IBM Watson Data Scientist v1

C1000-154 Online Dumps – IBM Watson Data Scientist v1

Share this post

Leave a Reply Cancel reply

Related Posts